Qwen3-VL Plus vs Qwen3 VL 235B A22B Thinking vs MiniMax-M2
Qwen3-VL Plus comes out ahead, 56 to 48 and 48 on our weighted score.
- Our pick
Alibaba (Qwen)Qwen3-VL Plus
56/100- ECI—
- Price$0.20 / $1.60
- Context262K
- Alibaba (Qwen)
Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
MiniMaxMiniMax-M2
48/100- ECI—
- Price$0.30 / $1.20
- Context205K
Qwen3-VL Plus is our pick
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against MiniMax-M2 (48) and Qwen3 VL 235B A22B Thinking (48). It leads on context window. Qwen3 VL 235B A22B Thinking wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMiniMax-M2MiniMax-M2 $0.525 · Qwen3-VL Plus $0.55 · Qwen3 VL 235B A22B Thinking $1.30 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL PlusQwen3-VL Plus 262,144 · MiniMax-M2 204,800 · Qwen3 VL 235B A22B Thinking 131,072 tokens
- Widest inputsQwen3-VL Plus and Qwen3 VL 235B A22B ThinkingQwen3-VL Plus: Text, Images · Qwen3 VL 235B A22B Thinking: Text, Images · MiniMax-M2: Text
- Self-hostingQwen3 VL 235B A22B Thinking and MiniMax-M2Publishes downloadable weights
| Measure | Weight | Qwen3-VL Plus | Qwen3 VL 235B A22B Thinking | MiniMax-M2 |
|---|---|---|---|---|
| Price | 50% | 62 | 44 | 63 |
| Inputs & features | 30% | 60 | 70 | 35 |
| Context window | 20% | 37 | 24 | 32 |
| Overall | 100% | 56/100 | 48/100 | 48/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3-VL PlusAlibaba (Qwen) | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) | MiniMax-M2MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.40 | $0.30 |
| Output | $1.60 | $4.00 | $1.20 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.55 | $1.30 | $0.525 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 9 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 262,144 tokens (best) | 131,072 tokens | 204,800 tokens |
| Max output | 32,768 tokens | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | qwen3-vl-plus | — | MiniMax-M2 |
| API providers | 6 | 9 | 13 (best) |
| Released | Sep 23, 2025 | Sep 23, 2025 | Oct 27, 2025 |
| Knowledge cutoff | Apr 2025 | Mar 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3-VL Plus$5.20
- Qwen3 VL 235B A22B Thinking$12.00
- MiniMax-M2$5.40
Which should you choose?
Which is better: Qwen3-VL Plus, Qwen3 VL 235B A22B Thinking or MiniMax-M2?
Qwen3-VL Plus is the better all-round choice, scoring 56/100 against MiniMax-M2 (48) and Qwen3 VL 235B A22B Thinking (48). It leads on context window. Qwen3 VL 235B A22B Thinking wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Qwen3-VL Plus, Qwen3 VL 235B A22B Thinking or MiniMax-M2?
MiniMax-M2 is cheaper at $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). Qwen3-VL Plus costs $0.20 input / $1.60 output per million tokens (official Alibaba API price); Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for MiniMax-M2 versus $0.55 for Qwen3-VL Plus (1× as much) and $1.30 for Qwen3 VL 235B A22B Thinking (2.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3-VL Plus has not been scored yet, Qwen3 VL 235B A22B Thinking has not been scored yet and MiniMax-M2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3-VL Plus, Qwen3 VL 235B A22B Thinking and MiniMax-M2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3-VL Plus has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2 and 131,072 for Qwen3 VL 235B A22B Thinking. Maximum output per response: Qwen3-VL Plus up to 32,768, Qwen3 VL 235B A22B Thinking up to 32,768, MiniMax-M2 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3-VL Plus accepts text and images; Qwen3 VL 235B A22B Thinking accepts text and images; MiniMax-M2 accepts text. Qwen3-VL Plus handles the widest range of inputs.
Are any of these open source?
Qwen3 VL 235B A22B Thinking and MiniMax-M2 publishes its weights and can be self-hosted; Qwen3-VL Plus is proprietary.
Which is newer?
MiniMax-M2 is the newest, released Oct 27, 2025. Qwen3-VL Plus came out Sep 23, 2025; Qwen3 VL 235B A22B Thinking came out Sep 23, 2025. Knowledge cutoff: Qwen3-VL Plus Apr 2025, Qwen3 VL 235B A22B Thinking Mar 31, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
